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Updated: Jul 5, 2025

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

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Published on: December 9, 2022

Structural and functional prediction, evaluation, and validation in the post-sequencing era.

Chang Li^1,2, Yixuan Luo³, Yibo Xie⁴

¹Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.

Computational and Structural Biotechnology Journal

|January 15, 2024

Summary

Interpreting genetic variants of uncertain significance (VUS) is challenging. Artificial intelligence (AI) offers efficient in silico prediction tools, particularly using protein structures, to accelerate VUS classification.

Keywords:

Artificial intelligence Clinical interpretation Missense variants Post-sequencing era Protein structure

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Genomics and Bioinformatics
Computational Biology
Artificial Intelligence in Medicine

Background:

The rapid increase in genome sequencing data has led to a large number of genetic variants of uncertain significance (VUS).
Classifying VUS is crucial for genetic diagnosis but remains a significant challenge due to the vast number of variants and limited experimental data.
Current experimental methods are insufficient to address the scale of VUS identified.

Purpose of the Study:

To review the current state of artificial intelligence (AI)-based prediction methods for missense variants.
To highlight the potential and challenges of using protein structure-based AI models for VUS interpretation.
To emphasize the need for advanced in silico functional predictors in the post-sequencing era.

Main Methods:

Review of existing literature on AI applications in VUS prediction.
Focus on AI models that utilize protein structure information.
Discussion of computational approaches for variant effect prediction.

Main Results:

AI demonstrates high efficiency and accuracy in predicting the functional impact of genetic variants.
Protein structure-based AI models show promise for improving VUS classification.
A significant gap exists between the number of identified VUS and experimentally validated variants.

Conclusions:

AI is a powerful tool for accelerating the interpretation of VUS.
Protein structure-based AI predictions offer a promising avenue for addressing the VUS challenge.
Further development and validation of in silico VUS predictors are urgently needed.